The invention relates to a method as recited in the preamble of claim 1. Various earlier translation methods have been designed. A relatively simple procedure is to use a translation code book, wherein each source item, such as a word or a phrase, maps on a single destination item. Such method is only useful for simple situations. A higher level method has been disclosed in [Berger & Brown .sup.+ 94] A. L. Berger, P. F. Brown, J. Cocke, S. A. Della Pietra, V. J. Della Pietra, J. R. Gillett, J. D. Lafferty, R. L. Mercer, H. Printz, L. Ures: "The Candide System for Machine Translation", ARPA Human Language Technology Workshop, Plainsboro, N.J., Morgan Kaufmann Publishers, San Mateo, Calif., pages 152-157, March 1994. [Brown & Pietra .sup.+ 93] P. F. Brown, S. A. Della Pietra, V. J. Della Pietra, R. L. Mercer: "Mathematics of Statistical Machine Translation: Parameter Estimation", Computational Linguistics, Vol. 19.2, pages 263-311, June 1993. It appears that the models governing the various word sequences could be improved upon, which also should allow raising the efficiency of the search procedures. In addition, there is a need for more simple procedures that will still cater to a high percentage of texts of intermediate complexity. Now, the present inventors have recognized the potential of dynamic programming and the technology of Hidden Markov Modelling that both have been used for speech recognition, as well as recognized various aspects that speech recognition and text translation in have in common.